JOURNAL ARTICLE

Noēsis and Diēgēsis: the Theory of Asystata and the Narrative in Ancient Rhetorical Treatises.

  • Published In: Scripta & e-Scripta: The Journal of Interdisciplinary Mediaeval Studies, 2024, v. 24. P. 259 1 of 3

  • Database: Central & Eastern European Academic Source 2 of 3

  • Authored By: Petrinski, Gerasim 3 of 3

Abstract

The article examines the interconnected theories of asystata (invalid or insoluble issues) in stasis theory and narrative coherence as developed in ancient rhetorical treatises, particularly within the progymnasmata exercises. Rooted in Aristotelian logic, these frameworks were used in Late Antiquity to identify criteria for invalid arguments and implausible narratives, thereby fostering critical thinking and effective communication. Key categories of invalidity include one-sidedness, impossibility, improbability, abrupt conversion, and inappropriateness, each corresponding to breaches of fundamental logical principles such as the Principles of Identity, Non-Contradiction, and Sufficient Reason. The study highlights how these rhetorical tools served both persuasive and educational purposes, shaping the development of European narrative theory by emphasizing plausibility over mere imagination. An appended comparative table aligns types of asystata with analogous narrative inconsistencies, illustrating their shared conceptual basis.

Additional Information

  • Source:Scripta & e-Scripta: The Journal of Interdisciplinary Mediaeval Studies. 2024/01, Vol. 24, p259
  • Document Type:Article
  • Subject Area:Literature and Writing
  • Publication Date:2024
  • ISSN:1312-238X
  • Accession Number:181588768
  • Copyright Statement:Copyright of Scripta & e-Scripta: The Journal of Interdisciplinary Mediaeval Studies is the property of Bulgarian Academy of Sciences, Institute of Literature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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